1. Introduction

With the advancement of science and technology, more businesses are leveraging data for their development. However, different enterprises at varying developmental stages adopt distinct methods for data management. Data assets, as relatively new assets aligned with the practical situations of most companies, have emerged. Yet, a definitive definition of data assets is yet to be unified. Nonetheless, this trend of increasing reliance on data assets for future business judgments remains unstoppable.

AF Entertainment primarily engages in film and television production and sales, where data assets play a crucial role in operational development and internal management. Establishing a data-driven business model is conducive to reducing transaction costs for the company and enhancing production efficiency.

Moreover, in future corporate development, the amalgamation of big data platforms and their derived data, combined with traditional financial models, can offer more referential predictive insights into the market. However, it's undeniable that research and exploration into data assets lag far behind other traditional accounting elements. Therefore, enterprises should prudently utilize data assets for their service while maintaining caution in their development.

2. Relevant Concepts and Introduction

Data assets, as the name suggests, comprise data and assets. Zhang Junrui et al. (2022) argued that clarifying the concepts of "soft assets" and "intangible assets," and exploring appropriate ways to report data assets in financial statements, could further clarify asset types. [1] Hu Yaru et al. (2022) believed that knowledge generated through data, when analyzed, can provide useful information for decision-making. [2] Huang Hai (2022) argued that effectively utilizing the value inherent in data assets within enterprises can benefit prospective users and reflect the current development status of the enterprise. [3]

In summary, data assets should encompass characteristics of both data and assets. As an asset that is expected to generate income for the enterprise but lacks physical form, it doesn't entirely align with traditional intangible assets. Data assets reflect the past activities of the enterprise and lack specific quantitative standards in the extraction process. The inherent data characteristics require mediation for usage, demanding a higher information processing capacity within the enterprise. As an emerging cross-resource within enterprises, data assets can be utilized. [4]

Before understanding the composition of data assets within an enterprise, specific data asset projects need clarification. Similar to traditional balance sheet classifications, data assets don't include products like audio-visual products. In the case of AF Entertainment, its produced audio-visual products like animated films, children's songs, etc., should be viewed as inventory awaiting sale. Data stored on computers or cloud drives under direct control of AF Entertainment, reflecting the enterprise's situation, also constitute data assets. For instance, personnel data in the backend and customer preference data in the frontend should be classified as data assets, requiring further processing and analysis by the enterprise. Furthermore, unlike traditional assets, data assets possess a strong replicability characteristic. Therefore, while analyzing the composition of data assets, this feature should be considered. [5]

When enterprises consider the composition of data assets, they should thoroughly contemplate whether data assets should encompass characteristics of tangible assets, intangible assets, long-term, and short-term assets, followed by subsequent processing based on their unique features.

3. Path to Realizing the Value of Data Assets

Since it's established that data assets can bring new advantages to enterprises and foster development, understanding how to maximize the effectiveness of data assets in production and operations becomes crucial. The introduction of data assets can offer a fresh perspective to traditional production chains within enterprises. Additionally, during the utilization of data assets, new data is generated, continually expanding the enterprise's data asset content. From the perspective of accelerating enterprise digital transformation, these data assets will facilitate the enterprise's digital construction.

In the initial stages of enterprise operations and production, before AF Entertainment embarks on creating new film and television entertainment programs, leveraging existing data assets for market analysis to understand current customer demands is essential. Moreover, utilizing accumulated data assets based on historical performance aids in analyzing competitors. Delving into data assets helps identify the enterprise's production advantages in that domain before production, facilitating the formulation of production plans in the most cost-effective manner, thereby directing cost reduction for the enterprise.

During the formal production phase, existing historical production experience within data assets can enable enterprises to leverage economies of scale better. Analyzing data assets from previous production stages allows the enterprise to swiftly identify highly efficient production methods. Moreover, amalgamating data from each year's production stages informs the enterprise about past production shortcomings. For instance, in film and television production, improving the production rate is a crucial aspect of delivering the final product. Analyzing data on past production durations helps save time in production stages.

During product sales, specific customer preferences contained within data assets, such as favored film and television themes, the desired duration of content, particularly considering AF Entertainment's primary audience being children and teenagers, play a pivotal role. Aside from creating content that appeals to younger audiences, it should also positively influence and attract parental guardians. Analyzing feedback data from past sales to guardian groups and adolescents helps tailor advertising in sales campaigns more effectively.

In the post-product sales stage, after a particular animated series is aired, establishing comprehensive market feedback channels and leveraging big data platforms to conduct preference model analyses allow for comprehensive summaries, facilitating preliminary investigations for the next stage of product preparation.

During enterprise review and strategic design, the reasonable utilization of data assets provides reference points for strategy formulation. Moreover, the mobility of data assets provides new growth points for enterprises in traditional production and sales pathways. The marginal returns of data assets continuously increase with each cycle within the enterprise, contributing to achieving economies of scale.

In summary, in realizing value from data assets, their integration with the enterprise's production chain allows for controlling the enterprise's production and development direction to a certain extent. This integration aids in achieving digitalized production, thereby further reducing production costs.

4. Data Asset Confirmation Management and Valuation Measurement

In confirming data assets, it's essential to assess whether these assets exhibit characteristics of measurability, relevance, and reliability within the asset properties. Regarding measurability, it's crucial to evaluate if data assets can be reliably measured, which relates to the valuation methods discussed in the previous chapter. This necessitates adopting relatively standardized valuation methods within the enterprise for measurement and refraining from easily altering established valuation methods. Relevance and reliability require data assets to effectively and accurately reflect their intrinsic value, contributing to relevant income in corresponding production activities [6].

As a film and television enterprise, a substantial portion of AF Entertainment's products constitutes data assets. Therefore, during the confirmation of data assets, several key aspects should be considered.

Firstly, establishing data asset verification standards aligned with the enterprise's development strategy is essential. In recent years, AF Entertainment's strategic focus has primarily revolved around establishing film and television intellectual property (IP) and selling maternal and infant products. Establishing reasonable data asset standards aids in providing reliable data support for the enterprise's maternal and infant product sales. Using these standards, segmenting data assets as required breaks down barriers between various data assets and traditional assets, making their utilization more convenient and highlighting the unique value of data assets.

Moreover, AF Entertainment should prioritize the development of a data asset management platform. After delineating and defining data assets, integrating them into a standardized management system is necessary. This system should include comprehensive criteria for identification, depreciation, amortization, and disposal. Within this platform, setting up standards for collection and coordination is vital to avoid inefficiencies resulting from scattered data assets. Data assets within this platform should seamlessly circulate across departments, eliminating related obstacles.

Due to the emergent nature of data assets and their distinctiveness from intangible assets, direct application of intangible asset valuation methods isn't feasible. However, as an asset category, data assets should adhere to an approach for initial and subsequent measurement.

For initial measurement, various methods are typically employed. Firstly, the historical cost method entails considering costs incurred before forming data assets, including expenses for collection, analysis, platform establishment, and required employee salaries. Additionally, if the enterprise hasn't internally formed its managed data assets and purchases data assets elsewhere, the payments made for these assets should be included in the cost when the enterprise needs to use data assets. The historical cost method provides a comprehensive reflection of the overall cost of the data asset, comparing all benefits flowing out and the income generated by the data asset. This comparison aids in reliably determining the effectiveness of the enterprise's utilization of data assets.

Another method is the fair value measurement method, where data assets can be compared to those of similar companies within the same industry to derive the data asset's value. This approach is applicable when it's challenging to estimate the value of data assets internally. Introducing fair value estimation can also involve referencing the market cost of these data assets, comparing them with existing asset values in the market, resulting in a relatively reasonable value for data assets.

If no similar assets' values are available in the market or among similar enterprises, an overall valuation within the production activity stage where data assets are utilized can be conducted. Costs associated with production activities should be separately analyzed, leading to an assessment of the data asset's value [7].

Regarding subsequent measurement of data assets, traditional asset items face issues such as impairment, depreciation, and amortization. However, for non-tangible assets like data assets, continued storage, sharing, and access consume costs. For instance, expenses for storing data on computers or subsequent platform maintenance payments require periodic assessment by enterprises. Determining amortization or depreciation amounts involves weighing whether the enterprise continues to incur costs for preserving and maintaining data assets. If a fair value estimation approach is adopted for data assets, periodic revaluations and impairment tests at fixed intervals are necessary to ascertain if impairment needs to be recognized.

As a listed company, AF Entertainment should regularly publish annual reports through standardized channels to disclose the company's operational status. Although the current financial reporting system doesn't provide detailed requirements for data assets, given their importance within enterprises, proper disclosure of data assets in financial reports is imperative. For example, AF Entertainment should mention in the report's notes the occurrence of data assets in the current period and whether depreciation, amortization, or impairment has occurred. If fair value changes are involved, these changes should be disclosed in the notes.

Additionally, when disclosing data assets, AF Entertainment should also release the valuation basis and treatment plans related to data assets, aiming to accurately reflect the status of data assets within the enterprise. AF Entertainment should also delineate the impact of data assets on its development strategy and the status of asset management platforms in the financial statements to enable users to comprehend the influence of data assets on the enterprise.

5. Current Issues and Countermeasures

The viewpoint that data assets can bring benefits to enterprises, expedite digital transformation, and establish big data platforms has been widely acknowledged by industry experts. However, the commercial data within data assets lacks a clear and detailed definition. Moreover, the rights and responsibilities of the relevant parties regarding data assets are not clearly defined, leading to disputes and unnecessary litigation costs in data usage. Furthermore, the characteristics of data assets, such as ease of replication, conflict with the enterprise's internal confidentiality agreements, increasing the risk of critical data leakage. Such conflicts could exacerbate the distrust of relevant users, making it challenging for data assets to circulate.

Regarding valuation methods for data assets, existing methods roughly demonstrate the value of corresponding data assets, yet these methods have limitations. Therefore, enterprises should explore methods suitable for valuing data assets within traditional valuation methods.

Government departments should take the lead in formulating industry-wide standards for data assets. Adapting to the actual development of enterprises, governmental departments should regularly communicate with enterprise representatives, continually establish laws and regulations to safeguard the legitimate rights and interests of enterprises concerning data assets. In the process of data asset transactions, the government could establish management departments to assist enterprises, facilitating transactions and providing information for subsequent supervision by governmental departments. The government can spearhead or commission qualified third parties to create inter-enterprise data asset trading platforms, aiding both in governmental management and reducing transaction costs for enterprises. Due to the inherent characteristics of data assets, stringent laws and regulations should be in place to protect confidentiality. When formulating laws to safeguard data asset security, governmental departments should pay particular attention to protecting trade secrets. [8]

The financial industry can reference traditional fixed asset pledges and guarantees to provide corresponding sales and pledge services for enterprises. In the financial system of data assets, financial institutions should possess the qualifications to retain data assets. This necessitates establishing a reasonable valuation system to assess the value of data assets. After holding data assets, financial institutions can engage in the sale of financial derivatives of data assets, promoting their activation and enhancing liquidity.

6. Conclusion and Limitations

This paper examines data assets as a novel concept within AF Entertainment, analyzing aspects such as value delineation, management, and subsequent disposal within film and television enterprises. It also identifies existing problems in data asset handling concerning enterprise development and proposes relevant solutions. In the current wave of data-driven transformation, data assets have become an indispensable part of enterprise strategies and will significantly impact future strategic implementations. However, due to personal limitations and the inability to directly engage with enterprises and insiders to obtain primary data, the viewpoints presented may not cover all aspects comprehensively.